Engineering Mathematics, University of Bristol and
University Hospitals Bristol NHS Foundation Trust
chris.mcwilliams@bristol.ac.uk
image = Image('resources/bd2k_0.jpg', width=700)
sh = display(image)
(Friedman, 2015)
(https://www.coursera.org/learn/machine-learning)
image = Image('resources/Supervised-Learning-versus-Unsupervised-Learning-Mathworks-nd.png', width=800)
sh = display(image)
(Bunker & Thabtah, 2017)
image = Image('resources/expert_system.png', width=800)
sh = display(image)
(www.igcseict.info/theory/7_2/expert/)
image = Image('resources/deeplearning1.png', width=700)
sh = display(image)
(https://www.ibm.com/blogs/systems/deep-learning-performance-breakthrough/)
image = Image('resources/deep_learning_growth.jpg', width=600)
sh = display(image)
"Artificial intelligence in healthcare: past, present and future" (Jiang et al, 2017)
image = Image('resources/black_box.png', width=800)
sh = display(image)
(https://callingbullshit.org/case_studies/case_study_ml_sexual_orientation_original_version.html)
But what about interpretability?
image = Image('resources/tools_used_in_ai.jpg', width=700)
sh = display(image)
"Artificial intelligence in healthcare: past, present and future" (Jiang et al, 2017)
....about 90% of my job.
(https://dataconomy.com/2016/03/why-your-datascientist-isnt-being-more-inventive/)
image = Image('resources/data_stress.jpg', width=700)
sh = display(image)
(https://the-modeling-agency.com/data-messy-dont-panic/)
In traditional machine learning - absolutely key.
In deep learning - not required!
image = Image('resources/feature_extraction.png', width=700)
sh = display(image)
image = Image('resources/train_validate_test.png', width=600)
sh = display(image)
(https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7)
image = Image('resources/tools_used_in_ai.jpg', width=700)
sh = display(image)
(Jiang et al, 2017)
(Vranas et al, 2017)
image = Image('resources/clustering_versus_classification.png', width=700)
sh = display(image)
image = Image('resources/subgroups_table.png') #, width=1500)
sh = display(image)
(Corrigan, Harush, Morgan, Shelim, Zulkarnaen, 2018)
Data: Physionet Challenge 2012
(https://physionet.org/challenge/2012/)
image = Image('resources/student_clusters.png') #, width=1500)
sh = display(image)
image = Image('resources/student_table.png', width=500)
sh = display(image)
image = Image('resources/nld_criteria_original.png', width=500)
sh = display(image)
"Nurseāled discharge from high dependency unit."
(Knight, 2003)
image = Image('resources/codified_nld.png')
sh = display(image)
"Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic health care data."
(McWilliams et al, 2019)
## thanks to: https://stackoverflow.com/questions/47637739/how-to-display-two-local-images-side-by-side-in-jupyter
## and border removal, thanks God! : https://github.com/ipython/ipython/issues/8581
display(HTML("<table style='border: 0'><tr style='border: 0'><td style='border: 0'><p style='border: none!important;'><img width='600' src='resources/time_panel_survivor_ptassess.png'></td><td style='border: 0'><img width='600' src='resources/time_panel_survivor_labres.png'></td></tr></table>"))
| ![]() |
display(HTML("<table style='border: 0'><tr style='border: 0'><td style='border: 0'><p style='border: none!important;'><img width='600' src='resources/time_panel_mortality_ptassess.png'></td><td style='border: 0'><img width='600' src='resources/time_panel_mortality_labres.png'></td></tr></table>"))
#display(HTML("<table><tr><td><img src='resources/time_panel_mortality_ptassess.png'></td><td><img src='resources/time_panel_mortality_labres.png'></td></tr></table>"))
| ![]() |
image = Image('resources/comparison_table.png')
sh = display(image)
image = Image('resources/cohort_table.png')
sh = display(image)
image = Image('resources/clustering_versus_classification.png', width=700)
sh = display(image)
image = Image('resources/tsne.png') #, width=1500)
sh = display(image)
image = Image('resources/figure1.png', width=1500)
sh = display(image)
image = Image('resources/performance_table_imputed.png')
sh = display(image)
image = Image('resources/fimp_table_imputed.png')
sh = display(image)
image = Image('resources/time_of_day_patterns.png') #, width=1500)
sh = display(image)
A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay (Kramer et al., 2010)
ICNARC (linear regression model).
Groups in Bristol (PICU, CICU).
image = Image('resources/rfd_board_whole.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_template_only.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_elements.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_1_&_8.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_1_&_8_contoured.png') #, width=1500)
sh = display(image)
image = Image('resources/tensor_flow_example.png', width=700)
sh = display(image)
(https://www.tensorflow.org/)
image = Image('resources/rfd_board_predictions.png') #, width=1500)
sh = display(image)
image = Image('resources/sevens.png') #, width=1500)
sh = display(image)
image = Image('resources/eye_scan_example.png', width=500)
sh = display(image)
"Clinically applicable deep learning for diagnosis and referral in retinal disease."
(De Fauw et al., 2018)
"Only 14,884 scans...""
"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer."
(Bejnordi et al., 2017)
"Interpretable Deep Models for ICU Outcome Prediction"
(Che et al., 2016)
"Recurrent neural networks for multivariate time series with missing values."
(Che et al., 2018)
image = Image('resources/gicu_dash.jpg', width=300)
sh = display(image)
image = Image('resources/increasing_compliance.jpg', width=500)
sh = display(image)
(make some slides on this but probably will skip them)
image = Image('resources/bd2k.jpg', width=700)
sh = display(image)
(Friedman, 2015)
image = Image('resources/new_gicu_whiteboard.jpg', width=400)
sh = display(image)